1. Field
The following description relates to technology that trains a neural network language model, and technology that performs speech recognition based on a language model.
2. Description of Related Art
Pattern recognition can be applied to various technologies, including the recognition of handwritten characters, medical diagnosis using imaging technologies, and detecting faults in machinery design. Human brain is capable of effortlessly recognizing patterns in visual images. However, performing efficient and accurate pattern recognition with a computer has been immensely difficult.
To classify an input pattern as a member that belongs to a predetermined group, researchers are actively conducting researches on the method of applying the efficient and accurate pattern recognition performed by people to a computer. One such area of research is focused on an artificial neural network that models characteristics of biological nerve cells of a human by mathematical expressions. To classify an input pattern as a predetermined group, the neural network employs an algorithm that simulates a learning capability of a human brain. Through this algorithm, the neural network may generate mapping between the input pattern and output patterns. The capability of generating such a mapping may be referred to as a learning capability of the neural network. Further, the neural network may have a generalization capability of generating a relatively accurate output with respect to an input pattern yet to be used for learning, based on a result of learning.
Technology that performs speech recognition using such a neural network is recently being studied. For example, researches are continuously conducted to increase an accuracy of speech recognition in various environments, such as an environment that includes a speech of a neighboring user or external noise.